Interpretive Summary: Adult Rocky Mountain wood ticks, Dermacentor andersoni, are vectors of the important tick-borne pathogen of cattle, Anaplasma marginale. In order to establish the risk of transmission of A. marginale, it is important to know the population density of the vector. Population density can be estimated by “dragging” for ticks, a quantitative sampling method based on dragging a one-meter square cloth over a known distance to sample a specific area of habitat. Because tick populations can be widely dispersed over a very great area drag sampling is inherently variable and it is often difficult to know how large a sample size is necessary for an accurate estimate of the population density. In this paper we use data collected from drag sampling at 13 locations in Alberta, Washington, and Oregon over 3 years to model the sample sizes needed to most accurately estimate the size of the tick population. A total of 222 samples were taken ranging in size from 86 – 250 10 sq. m. quadrats. Simulated quadrats ranging in size from 20 – 50 sq. m. indicated that none of these sizes provided estimates that were more precise than using 10 sq. m quadrats. Samples taken when abundance was less than 0.04 ticks per 10 sq. m. were more likely to appear to be random than samples taken when abundance was greater. Three different models were fit to the data and then used to predict sample sizes needed for a fixed level of precision. A negative binomial with common k provided estimates of required sample sizes that were closest to actual sample sizes.

Technical Abstract:
Off-host populations of adult Rocky Mountain wood tick, Dermacentor andersoni (Stiles) were sampled by dragging at 13 locations in Alberta, Washington, and Oregon. A total of 222 samples were taken ranging in size from 86 – 250 10 sq. m. quadrats. Simulated quadrats ranging in size from 20 – 50 sq. m. indicated that none of these sizes provided estimates precise than using 10 sq. m quadrats. An index of dispersion test indicates that samples taken when abundance is less than 0.04 ticks per 10 sq. m. were more likely to not depart significantly from randomness than samples taken when abundance was greater. Data were grouped into ten abundance classes assessed for fit to the Poisson and negative binomial distributions. The Poisson distribution fit only data in abundance classes less than 0.02 ticks per 10 sq. m, while the negative binomial distribution fit data from all abundance classes. A negatives binomial with common k = 0.3742 fit data in 8 of the 10 abundance classes. Both the Taylor and Iwao mean-variance relationships were fit and used to predict sample sizes for a fixed level of precision. Sample sizes predicted using the Taylor model tended to underestimate actual sample sizes, while sample sizes estimated using the Iwao model tended to overestimate actual sample sizes. Using a negative binomial with common k provide estimates of required sample sizes closest to actual sample sizes.